Digital twin workshop production logistics real-time scheduling management method and system

Abstract

The application relates to the technical field of production logistics scheduling management, and discloses a digital twin workshop production logistics real-time scheduling management method and system, which comprises the following steps: step 1, establishing unified state data and standard parameter baseline, calculating consistency deviation value and setting two-stage deviation determination threshold; step 2, receiving on-site events and determining a scheduling time window; step 3, determining task priority and generating a production to-be-executed list; step 4, generating a carrying task set from the production to-be-executed list and determining carrying equipment and task allocation under constraints; step 5, carrying out channel conflict-free path planning and generating a time-space occupation table; step 6, carrying out cross-consistency checking and executing issuing or local recalculation according to the threshold; and step 7, executing on-site operation and updating unified state data, and executing consistency recovery when the deviation exceeds the limit. The application realizes real-time collaborative scheduling and high-reliability operation of workshop production and logistics tasks.

Description

Technical Field

[0001] This invention belongs to the field of production logistics scheduling and management technology, specifically relating to a method and system for real-time scheduling and management of production logistics in a digital twin workshop. Background Technology

[0002] Manufacturing enterprises in discrete manufacturing scenarios typically face challenges such as numerous processes, complex equipment types, overlapping logistics paths, and unstable task arrival times. As product customization increases and order volume rises, key parameters such as the quantity of work-in-process in the workshop, the length of workstation queues, equipment operating status, and the load on handling equipment exhibit high-frequency fluctuations. Traditional scheduling methods that rely on manual experience or fixed rules often fail to respond promptly to changes on-site, easily leading to process waiting, equipment idling, and logistics channel congestion, which in turn results in disrupted production rhythms and reduced resource utilization.

[0003] The rise of digital twin technology has provided manufacturing systems with bidirectional virtual reality mapping capabilities, enabling the simulation, prediction, and optimization of shop floor conditions. However, differences exist between virtual models and actual data, such as sensing errors, synchronization delays, and event disturbances. Without an effective mechanism for monitoring and correcting virtual-real consistency, the virtual model will fail to accurately reflect the actual situation, leading to deviations between scheduling strategies and actual operating conditions. Simultaneously, production tasks and logistics handling tasks are highly coupled; changes in production plans affect the generation of logistics tasks and the paths of handling equipment; logistics delays, in turn, affect workstation load and production rhythm, making scheduling problems even more complex in a dynamic environment.

[0004] The application of autonomous handling equipment improves the flexibility of workshop logistics, but task allocation must simultaneously meet constraints such as load capacity, power consumption, and clamping compatibility, and avoid path conflicts in shared channels. Without spatiotemporal analysis of channel occupancy and conflict resolution mechanisms, equipment congestion or chaotic yielding can easily occur. Furthermore, frequent global recalculations by the scheduling system will cause scheduling jitter, affecting production continuity. Summary of the Invention

[0005] This invention provides a method and system for real-time scheduling and management of production logistics in a digital twin workshop, which solves the technical problems in related technologies, such as scheduling decision deviations caused by the asynchrony between virtual and real states, insufficient coordination caused by the separation of production and logistics tasks, difficulty in task allocation under the constraints of handling equipment, and inability to handle channel congestion and path conflicts in real time.

[0006] This invention provides a method for real-time scheduling and management of production logistics in a digital twin workshop, comprising the following steps:

[0007] Step 1: Establish a baseline of unified state data and standard parameters, calculate the consistency deviation value based on the unified state data and standard parameter baseline, and set two levels of deviation judgment thresholds;

[0008] Step 2: Receive field events and determine the scheduling time window based on unified status data, standard parameter baseline, consistency deviation value, and field events;

[0009] Step 3: Determine priorities based on standard parameter baselines and unified status data, and generate a production to-do list;

[0010] Step 4: Generate a set of handling tasks from the production to be executed list, and determine the allocation results of handling equipment and handling tasks under the constraints of load, power consumption and clamping compatibility;

[0011] Step 5: Based on the allocation results of handling equipment and handling tasks, perform conflict-free path planning on the channel to form a time-space occupancy table;

[0012] Step 6: Perform cross-consistency checks based on consistency deviation values, time and space occupancy tables, and upper limits of each workstation queue; generate deployable production operation sequences and deployable handling equipment tasks based on the first and second thresholds; and recalculate local objects involving deviations.

[0013] Step 7: Perform on-site operations based on the available production operation sequence, available handling equipment tasks, and time and space occupancy table; collect receipt data and update unified status data; when the consistency deviation value is higher than the second threshold, perform consistency restoration.

[0014] Furthermore, a unified state data and standard parameter baseline are established, and the consistency deviation value is calculated based on the unified state data and standard parameter baseline, including:

[0015] Collect the quantity of work-in-process, the length of each workstation queue, the location of handling equipment, and the operating status of the equipment, and align the timestamps to form unified status data; record the process set, workstation set, standard processing time, loading and unloading and changeover time, channel capacity, and upper limit of workstation queue according to a fixed parameter structure as a standard parameter baseline;

[0016] The field value of each field in the unified state data is compared one by one with the virtual value of the corresponding field in the twin virtual state to generate the difference quantity; all the difference quantities are linearly weighted and accumulated according to the preset weight and normalization coefficient to form the consistency deviation value.

[0017] Furthermore, it receives field events and determines scheduling time windows based on unified status data, standard parameter baselines, consistency deviation values, and field events, including:

[0018] Step 11: Obtain on-site events from the production workshop, where each on-site event contains an event type and a timestamp; organize the on-site events into a set according to the event type and in chronological order, and assign a unique identifier and a corresponding timestamp to each event to form an on-site event set;

[0019] Step 12: Project the real-time queue information in the unified status data onto each channel, calculate the proportion of the excess capacity of each channel to the total capacity of all channels, and obtain the channel congestion coefficient.

[0020] Step 13: The scheduling time window is obtained by weighting the basic time window, the channel congestion coefficient, and the event impact coefficient. The event impact coefficient is determined based on the type and number of on-site events.

[0021] Furthermore, based on standard parameter baselines and unified status data, task priorities are determined, and a production to-do list is generated, including:

[0022] Step 21: Calculate the sum of the standard processing times of all remaining processes in the order as the remaining time for each production task, and calculate the difference between the order deadline and the remaining time as the slack of the order.

[0023] Step 22: Calculate the absolute difference between the current queue length and the target queue length at each workstation, and divide it by the target queue length to obtain the queue deviation for each workstation.

[0024] Step 23: By weighted summing of order slackness, workstation queue deviation and task urgency, the priority of each task is obtained, and a production list to be executed is generated in order of priority.

[0025] Furthermore, a set of handling tasks is generated from the production pending list, and the allocation of handling equipment and tasks is determined under constraints of load capacity, power consumption, and clamping compatibility, including:

[0026] Step 31: Based on the workstations to be processed for each order in the production pending list, generate corresponding handling tasks to form a handling task set. Each handling task includes a material pick-up point, a material feeding point, and a time window.

[0027] Step 32: Calculate the sum of the distance from the location of the handling equipment to the picking point and the distance from the picking point to the feeding point, divide it by the speed of the handling equipment, and add the loading and unloading time of the handling task and the docking time of the handling equipment after completing the task to obtain the handling cost.

[0028] Step 33: Under the conditions of satisfying load constraints, power constraints, and clamping compatibility constraints, match the handling tasks with the handling equipment, calculate the sum of the handling costs of all handling tasks, obtain the total cost, and obtain the final allocation result by minimizing the total cost.

[0029] Furthermore, based on the allocation results of handling equipment and handling tasks, conflict-free path planning is performed on the channel to form a spatiotemporal occupancy table, including:

[0030] Step 41: Based on the allocation results of handling equipment and handling tasks, calculate the path of each handling equipment, including from the location of the handling equipment to the picking point, from the picking point to the feeding point, and from the feeding point to the next task location of the handling equipment. Use the shortest path algorithm to calculate the travel time of each path.

[0031] Step 42: Based on the length of the transport equipment path and the equipment travel speed, calculate the time occupied by each path and the travel time of the transport equipment through the path to obtain the departure time. Based on the entry and exit positions of the path, record the time and space occupancy of each path and generate a time and space occupancy table.

[0032] Step 43: By detecting the time and space occupancy of the transport equipment path, determine whether there is a path conflict. If there is a conflict, prioritize adjusting the path with lower priority and use the second shortest path for adjustment.

[0033] Furthermore, a cross-consistency check is performed based on the consistency deviation value, the time-space occupancy table, and the upper limit of each workstation queue. Based on the first and second thresholds, a production operation sequence and a handling equipment task that can be issued are generated. Local objects involving deviations are recalculated, including:

[0034] Step 51: Calculate the number of materials fed and discharged within the scheduling time window, and use the current queue length to add the number of materials fed and subtract the number of materials discharged to obtain the new queue length for the workstation;

[0035] Step 52: When the consistency deviation value is less than or equal to the first threshold, there is no conflict in the time and space occupancy table of all channels, and the new queue length of all workstations is less than or equal to the upper limit of the queue, generate a production operation sequence that can be issued and a handling equipment task that can be issued, and issue the scheduling plan.

[0036] Step 53: When the consistency deviation value is greater than the first threshold and less than or equal to the second threshold, perform a partial recalculation on the workstations, handling equipment, and passages related to the deviation, and recalculate the logistics task assignment and route planning.

[0037] Further, perform on-site operations, collect receipt data, and update the unified status data; when the consistency deviation value exceeds the second threshold, perform consistency recovery, including:

[0038] The production side executes operations based on the available production operation sequence, while the logistics side executes equipment handling based on the available handling equipment tasks and the time and space occupancy table. On-site, feedback data is collected in real time, including the unique identifier of the task, event type, timestamp, number of completed processes, and workstation location. The feedback data is updated to the unified status data in real time. If a path conflict occurs between the handling equipment, it is handled according to the static yielding rules and the time and space occupancy table is updated synchronously.

[0039] When the consistency deviation value is greater than the second threshold, the unified status data is corrected using the receipt data.

[0040] Furthermore, during the production process, the progress of each task is tracked in real time, and the resource allocation of handling equipment and workstations is automatically adjusted according to the completion status of the tasks. When a workstation completes its task ahead of schedule, the handling equipment at that workstation is automatically scheduled to perform the next task. When a workstation task is delayed, the scheduling time window of the task is adjusted.

[0041] This invention provides a real-time scheduling and management system for production logistics in a digital twin workshop, comprising:

[0042] The Unified State and Baseline module is used to establish a baseline between unified state data and standard parameters, calculate the consistency deviation value based on the unified state data and standard parameter baseline, and set two levels of deviation judgment thresholds.

[0043] The on-site event scheduling module is used to receive on-site events and determine the scheduling time window based on unified status data, standard parameter baseline, consistency deviation value and on-site events;

[0044] The priority generation module is used to determine priorities based on standard parameter baselines and unified status data, and generate a production execution list.

[0045] The material handling task allocation module is used to generate a set of material handling tasks from the production to be executed list, and determine the allocation results of material handling equipment and material handling tasks under the constraints of load, power consumption and clamping compatibility;

[0046] The path planning module is used to perform conflict-free path planning on the channel based on the allocation results of handling equipment and handling tasks, and to form a time and space occupancy table.

[0047] The consistency check module is used to perform cross-consistency checks based on consistency deviation values, time and space occupancy tables and the upper limit of each workstation queue, and to generate a production operation sequence and a handling equipment task that can be issued based on the first threshold and the second threshold, and to recalculate local objects involving deviations.

[0048] The execution and receipt module is used to execute on-site operations based on the available production operation sequence, available handling equipment tasks, and time and space occupancy table, collect receipt data, and update unified status data; when the consistency deviation value is higher than the second threshold, consistency recovery is performed.

[0049] The beneficial effects of this invention are as follows: By constructing unified state data and standard parameter baselines, this invention achieves standardized integration of multi-source information from the production and logistics sides, providing accurate and complete real-time input for the digital twin model; by calculating consistency deviation values ​​and setting two-level deviation judgment thresholds, it can dynamically monitor the matching degree of virtual and real states, avoiding scheduling jitter caused by global recalculation and ensuring stable system operation. By introducing an event-driven scheduling time window mechanism, scheduling calculations can adaptively adjust according to on-site events and channel congestion, improving the real-time performance and targeting of scheduling. A production to-do list is generated based on remaining working hours, slack, and queue deviation, achieving precise quantification of production needs. By allocating handling tasks under constraints of load, power, and clamping compatibility, and combining conflict-free path planning and time-space occupancy management, the handling process is ensured to be safe, efficient, and controllable. Overall, this invention achieves integrated scheduling of production and logistics, synergy between virtual and real consistency control and local dynamic optimization, improving workshop operating efficiency and resource utilization, and enhancing the reliability and real-time response capability of the scheduling system in complex manufacturing scenarios. Attached Figure Description

[0050] Figure 1 This is a flowchart of the real-time scheduling and management method for production logistics in a digital twin workshop according to the present invention. Detailed Implementation

[0051] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the subject matter described herein, and changes may be made to the function and arrangement of the elements discussed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the examples. Furthermore, features described in some examples may be combined in other examples.

[0052] It should be noted that, unless otherwise defined, the technical or scientific terms used in one or more embodiments of the present invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in one or more embodiments of the present invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0053] like Figure 1 As shown, the real-time scheduling and management method for production logistics in a digital twin workshop includes the following steps:

[0054] Step 1: Establish a baseline of unified state data and standard parameters, calculate the consistency deviation value based on the unified state data and standard parameter baseline, and set two levels of deviation judgment thresholds;

[0055] Step 2: Receive field events and determine the scheduling time window based on unified status data, standard parameter baseline, consistency deviation value, and field events;

[0056] Step 3: Determine priorities based on standard parameter baselines and unified status data, and generate a production to-do list;

[0057] Step 4: Generate a set of handling tasks from the production to be executed list, and determine the allocation results of handling equipment and handling tasks under the constraints of load, power consumption and clamping compatibility;

[0058] Step 5: Based on the allocation results of handling equipment and handling tasks, perform conflict-free path planning on the channel to form a time-space occupancy table;

[0059] Step 6: Perform cross-consistency checks based on consistency deviation values, time and space occupancy tables, and upper limits of each workstation queue; generate deployable production operation sequences and deployable handling equipment tasks based on the first and second thresholds; and recalculate local objects involving deviations.

[0060] Step 7: Perform on-site operations based on the available production operation sequence, available handling equipment tasks, and time and space occupancy table; collect receipt data and update unified status data; when the consistency deviation value is higher than the second threshold, perform consistency restoration.

[0061] In one embodiment of the present invention, a unified state data and standard parameter baseline are established, and a consistency deviation value is calculated based on the unified state data and standard parameter baseline. Two levels of deviation judgment thresholds are set, including:

[0062] The system collects data on work-in-process quantity, queue length at each workstation, location of handling equipment, and equipment operating status, and aligns these data with timestamps to form unified status data. Work-in-process quantity refers to the number of workpieces in the workshop that are being processed, waiting to be processed, or being transported. Queue length at each workstation refers to the number of workpieces waiting to be processed at each processing workstation. Equipment operating status includes the start / stop status of processing equipment and the specific location information of any malfunctions. The system records the process set, workstation set, standard processing time, loading / unloading and changeover time, channel capacity, and workstation queue limit as a standard parameter baseline according to a fixed parameter structure. The fixed parameter structure includes field order, field type, and storage format to ensure the standardization and reusability of the standard parameter baseline. Among these parameters, standard processing time represents the preset standard processing time for each process at the corresponding workstation; loading / unloading and changeover time represents the loading and unloading time of workpieces at the workstation and the adjustment time when switching processing processes at the workstation; channel capacity represents the maximum number of handling equipment that can pass through each transport channel in the workshop simultaneously; and workstation queue limit represents the maximum number of workpieces that can wait at each workstation.

[0063] The on-site value of each field in the unified state data is compared one by one with the virtual value of the corresponding field in the digital twin state to generate a difference quantity. All differences are linearly weighted and accumulated according to preset weights and normalization coefficients to form a consistency deviation value. The on-site value is the real data actually collected in the workshop, and the virtual value is the corresponding state data simulated in the digital twin model. The normalization coefficient is determined according to the value range of the field. At the same time, two levels of deviation judgment thresholds are set, namely the first threshold and the second threshold, and the first threshold is less than the second threshold. The first threshold is the critical value at which the scheduling plan can be directly issued and executed, and the second threshold is the critical value at which the local recalculation and consistency recovery are distinguished.

[0064] The consistency deviation value is used to quantify the overall deviation between the virtual state of the digital twin and the actual state of the workshop. Essentially, it is a weighted distance measurement of the virtual and real states within a unified index space. By calculating the differences between the actual and virtual values ​​for multiple key fields such as work-in-process quantity, workstation queue length, handling equipment location, and operating status, and then normalizing and weighting these values, state parameters with different dimensions and varying degrees of change can be comprehensively evaluated on the same scale. This avoids misleading overall judgments due to anomalies in a single field.

[0065] The consistency deviation value, as a core control variable in the digital twin scheduling system, directly participates in the determination of scheduling decisions and execution strategies. When the consistency deviation value is below the first threshold, it indicates that the digital twin virtual state can accurately reflect the on-site operating state, the scheduling results generated based on the virtual state have high reliability, and the scheduling scheme can be directly issued for execution. When the consistency deviation value is between the first and second thresholds, it indicates that there is a local deviation between the virtual and real states. The system only performs local recalculation on the workstations, handling equipment, or channels associated with the deviation to suppress the spread of the deviation and maintain the overall scheduling stability. When the consistency deviation value exceeds the second threshold, it indicates that there is a significant mismatch between the virtual and real states. The system prioritizes the consistency restoration of the unified state data and the twin virtual state based on the on-site feedback data to avoid making scheduling decisions based on a distorted model.

[0066] Through the above methods, the consistency deviation value forms a closed-loop control mechanism between scheduling calculation, execution issuance and status update, so that the virtual state of the digital twin is always constrained by the real state on site, preventing the virtual model from drifting over time, thereby ensuring the consistency of the virtual and real states and the reliability of scheduling decisions in the production logistics scheduling process of the digital twin workshop from the system level.

[0067] This embodiment achieves the integration and collaboration of multi-source data by constructing unified state data. The establishment of standard parameter baselines provides a stable basis for scheduling calculations with deterministic fixed parameters. By setting consistency deviation values ​​and two-level thresholds, the degree of consistency between the digital twin model and the workshop site can be accurately quantified, ensuring the robustness and accuracy of real-time scheduling of production logistics in the digital twin workshop from the data level.

[0068] In one embodiment of the present invention, receiving field events and determining a scheduling time window based on unified status data, standard parameter baselines, consistency deviation values, and field events includes:

[0069] Step 11: Obtain on-site events from the production workshop. These on-site events are real-time signals reflecting abnormal production logistics status or changes in demand in the workshop. Each on-site event includes an event type and a timestamp. On-site event types include equipment downtime, urgent orders, queue exceeding limits, etc. Based on the event type, the on-site events are organized into a set in chronological order, and each event is assigned a unique identifier and a corresponding timestamp to form an on-site event set, ensuring the orderly flow of on-site dynamic information.

[0070] Step 12: Project the real-time queue information in the unified status data onto each channel. That is, based on the workshop layout and logistics route planning, determine the specific transportation channel on which the workpieces at each workstation enter and exit. Then, count the real-time queue correlation quantity corresponding to each channel, calculate the proportion of the excess capacity of each channel to the total capacity of all channels, and obtain the channel congestion coefficient. The channel congestion coefficient ranges from 0 to 1. The larger the value, the higher the congestion level of the overall transportation channels in the workshop, which can quantify the busy state of logistics transportation.

[0071] Step 13: The scheduling time window is obtained by weighting the basic time window, the channel congestion coefficient, and the event impact coefficient. The event impact coefficient is used to quantify the adjustment of the scheduling time window by on-site events. It is determined according to the type and number of on-site events. For example, the event impact coefficient is higher for critical events such as urgent orders and equipment downtime. The more events there are, the larger the value of the event impact coefficient. The final scheduling time window can dynamically match the degree of on-site congestion and the urgency of events, ensuring that the scheduling plan has enough optimization time and avoiding scheduling delays due to excessively long time windows.

[0072] This embodiment achieves precise capture of dynamic changes in the workshop by structuring and organizing on-site events, realizes quantitative assessment of logistics and transportation status by calculating channel congestion coefficients, and ultimately achieves precise matching between on-site and scheduling time by weighted generation of time windows, ensuring the timeliness and pertinence of real-time scheduling of production logistics in the digital twin workshop.

[0073] In one embodiment of the present invention, task priorities are determined based on standard parameter baselines and unified status data, and a production to-be-executed list is generated, including:

[0074] Step 21: Calculate the sum of the standard processing times of all remaining processes in the order as the remaining time for each production task, and calculate the difference between the order deadline and the remaining time as the slack of the order.

[0075] Step 22: Calculate the absolute difference between the current queue length and the target queue length of the workstation, and divide it by the target queue length to obtain the queue deviation of each workstation, which is used to quantify the degree of workstation load deviation; where the target queue length is 0, it is calculated as 1.

[0076] Step 23: By weighted summing of order slackness, workstation queue deviation and task urgency, the priority of each task is obtained, and a production list to be executed is generated in order of priority.

[0077] This embodiment calculates priority indicators based on deterministic standard parameters and real-time status data, avoiding the subjectivity and experience dependence of priority judgment in traditional scheduling. Remaining working hours and relaxation time quantify order delivery pressure, while queue deviation quantifies workstation load. The weighted integration of these three factors ensures that priority ranking not only matches the urgency of order requirements but also adapts to the actual operating status of workstations, ensuring the accuracy and rationality of the production to-do list. This process provides a clear basis for the generation and assignment of subsequent logistics and handling tasks, guaranteeing the orderliness and efficiency of real-time scheduling of production logistics in the digital twin workshop.

[0078] In one embodiment of the present invention, a set of handling tasks is generated from a production to-be-executed list, and the allocation result of handling equipment and handling tasks is determined under constraints of load capacity, power consumption, and clamping compatibility, including:

[0079] Step 31: Based on the workstations to be processed for each order in the production to be executed list, match the raw material storage location and workstation layout information in the standard parameter baseline to generate corresponding handling tasks to form a handling task set. Each handling task includes a material pick-up point, a material feeding point, and a time window. The material pick-up point is the storage location of the raw materials required by the order, the material feeding point is the workstation to be processed for the order, and the time window is the scheduling time window interval that the handling task needs to complete.

[0080] Step 32: Calculate the sum of the distance from the location of the handling equipment to the picking point and the distance from the picking point to the feeding point, divide it by the speed of the handling equipment, add the loading and unloading time of the handling task and the docking time of the handling equipment after completing the task, and obtain the handling cost. The handling cost reflects the degree of resource consumption in the execution of the task.

[0081] Step 33: Under the conditions of satisfying load constraints, power constraints, and clamping compatibility constraints, match the handling tasks with the handling equipment, and calculate the sum of the handling costs of all handling tasks to obtain the total cost. By minimizing the total cost, the final allocation result is obtained, ensuring optimal resource consumption and that all tasks can be executed in compliance with regulations. Load constraints mean that the rated load of the handling equipment is not less than the weight of the workpiece being transported; power constraints mean that the remaining power of the handling equipment is sufficient for the power consumption required to complete the task's travel and loading / unloading; clamping compatibility constraints mean that the clamping mechanism of the handling equipment matches the workpiece type to ensure stable handling.

[0082] This embodiment achieves seamless linkage between production needs and logistics tasks by directly generating transportation tasks from the production to be executed list, solving the task mismatch problem caused by the disconnect between production and logistics in traditional scheduling. The calculation of transportation costs ensures the accuracy of cost assessment and achieves the minimum allocation of total cost under multiple hard constraints. This not only ensures the safety and feasibility of the transportation process, but also reduces logistics operating costs, ensuring the economy and efficiency of real-time scheduling of production logistics in the digital twin workshop.

[0083] In one embodiment of the present invention, conflict-free path planning is performed on the channel based on the allocation results of handling equipment and handling tasks to form a spatiotemporal occupancy table, including:

[0084] Step 41: Based on the allocation results of handling equipment and handling tasks, calculate the path of each handling equipment, including from the location of the handling equipment to the picking point, from the picking point to the feeding point, and from the feeding point to the next task location of the handling equipment. Use the shortest path algorithm to calculate the travel time of each path to ensure the efficiency of path planning.

[0085] Step 42: Based on the length of the transport equipment path and the equipment travel speed, calculate the time occupied by each path and the travel time of the transport equipment through the path to obtain the departure time. Based on the entry and exit positions, associate the corresponding channel identifier and transport equipment identifier to record the time and space occupancy of each path, i.e., from the entry time to the exit time, and generate a time and space occupancy table. The time and space occupancy table is a structured table used to record the occupancy of each channel.

[0086] Step 43: By detecting the spatial and temporal occupancy of the transport equipment paths, it is determined whether there are path conflicts. If a conflict exists, the path with lower priority is adjusted first, using the next shortest path. The travel time and spatial and temporal occupancy intervals are recalculated, and conflicts are detected again until all channels are free of overlapping occupancy, ensuring conflict-free path planning. Path conflict refers to the situation where the spatial and temporal occupancy intervals of different transport equipment overlap in the same channel.

[0087] This embodiment achieves high efficiency and visual management of path planning through the shortest path algorithm and time-space occupancy quantification, solving the problems of blind path planning and frequent conflicts in traditional scheduling. Conflict handling follows task priority rules to ensure the logistics guarantee of high-priority production needs. The combination of conflict-free path planning and time-space occupancy table avoids channel congestion and equipment collisions, ensuring the reliability and execution efficiency of real-time scheduling.

[0088] In one embodiment of the present invention, a cross-consistency check is performed based on the consistency deviation value, the time-space occupancy table, and the upper limit of each workstation queue. Based on a first threshold and a second threshold, a production operation sequence and a handling equipment task that can be issued are generated. The local objects involving the deviation are recalculated, including:

[0089] Step 51: Calculate the number of materials fed in and discharged within the scheduling time window. Add the number of materials fed in to the current queue length and subtract the number of materials discharged to obtain the new queue length for the workstation. The number of materials fed in comes from the number of workpieces planned to be delivered to each workstation in the handling task set, and the number of materials discharged comes from the number of workpieces planned to be processed by each workstation in the production pending execution list. The new queue length is used to predict whether the workstation exceeds the limit.

[0090] Step 52: The cross-consistency check is used to perform multi-dimensional verification of the consistency between the digital twin virtual state and the on-site executable state before the scheduling plan is issued. It is used not only to determine the logical feasibility of the scheduling results but also to constrain the deviation between the virtual scheduling simulation results and the physical operating state of the workshop. The core requirements are three conditions: first, the consistency deviation value is less than or equal to the first threshold, ensuring that the virtual and real states match the standard; second, there are no conflicts in the spatiotemporal occupancy tables of all channels, ensuring smooth operation of the handling equipment; and third, the new queue length of all workstations is less than or equal to the upper limit of the workstation queue set in the standard parameter baseline, avoiding overload operation of the workstations. When all three conditions are met, a production operation sequence and a handling equipment task can be issued. The production operation sequence clarifies the task execution order and start time of each workstation, while the handling equipment task clarifies the path and loading / unloading time of each handling equipment. These are integrated to form a complete scheduling plan, including core information such as task execution order, equipment allocation, path planning, and resource scheduling, and then issued to the on-site execution.

[0091] Step 53: When the consistency deviation value is greater than the first threshold and less than or equal to the second threshold, perform local recalculation on the workstations, handling equipment and channels related to the deviation, recalculate the logistics task assignment and conflict-free path planning, and keep the scheduling results of other tasks not involved in the deviation unchanged to ensure the overall stability of the system.

[0092] This embodiment avoids the risk of infeasibility caused by single-dimensional verification through multi-dimensional cross-consistency checks; based on two-level threshold differentiation, it ensures the executability of the scheduling scheme and avoids scheduling jitter caused by global recalculation, thus maintaining system stability; the local recalculation mechanism significantly reduces the consumption of computing resources, ensures the response efficiency of real-time scheduling, and enhances the robustness and accuracy of production logistics scheduling in the digital twin workshop, providing a key guarantee for the efficient implementation of the scheduling scheme.

[0093] In one embodiment of the present invention, on-site operations are performed based on the deployable production operation sequence, deployable handling equipment tasks, and time-space occupancy table; receipt data is collected and unified status data is updated; when the consistency deviation value is higher than a second threshold, consistency recovery is performed, including:

[0094] The production side executes operations based on the available production work sequence, while the logistics side executes equipment handling based on the available handling equipment tasks and the time and space occupancy table. This ensures the coordinated advancement of production and logistics. Real-time feedback data is collected on-site, including the unique task identifier, event type, timestamp, number of completed processes, and workstation location. The feedback data is then updated to a unified status data in real time. If a path conflict occurs between handling equipment, it is handled according to the static yielding rule, and the time and space occupancy table is updated synchronously. That is, the main vehicle keeps straight ahead, while the secondary vehicle waits in the upstream area of ​​the passage for a preset fixed time before continuing. At the same time, the occupancy range of the corresponding equipment in the time and space occupancy table is updated synchronously to avoid the conflict from escalating.

[0095] When the consistency deviation value is greater than the second threshold, the acknowledgment data is used to correct the corresponding state in the unified state data and the twin virtual entity to avoid global recalculation. Only precise alignment at the data level is performed without triggering global recalculation, ensuring the overall stable operation of the system.

[0096] In this embodiment, consistency recovery is used to forcibly correct the virtual model when there is a significant mismatch between the virtual state of the digital twin and the actual state of the workshop site, in order to prevent the virtual state from continuously deviating from the operating state of the physical entity. Consistency recovery does not perform a global recalculation of the scheduling logic. Instead, it uses the field feedback data as a benchmark to align and update the corresponding fields in the unified state data and the virtual twin state, so that the virtual model returns to the real state of the field.

[0097] This embodiment solves the problems of disconnect between virtual and real states and data lag in traditional scheduling by updating and dynamically correcting acknowledgment data in real time; static yielding rules avoid congestion or collisions of handling equipment, ensuring the smooth execution of logistics; the consistency recovery mechanism enables the digital twin system to quickly realign virtual and real states through the reverse constraint of physical entities on the virtual model in the event of emergencies, execution delays or abnormal disturbances, thereby preventing the virtual model from drifting over time, maintaining the reliability of the state and the effectiveness of scheduling decisions in the long-term operation of the digital twin system, and providing key guarantees for the long-term operation of the entire scheduling management system.

[0098] In one embodiment of the present invention, during the production operation, the completion progress of each task is tracked in real time. The task completion progress is the proportion of completed processes to the total remaining processes. Based on the completion status of the task execution, the resource allocation of handling equipment and workstations is automatically adjusted. When a workstation completes a task ahead of schedule, the handling equipment of that workstation is automatically scheduled to execute the next task. When a workstation task is delayed due to equipment failure, material abnormality, or other reasons, the system dynamically adjusts the scheduling time window of the task based on the real-time statistics of the delay duration, combined with the execution status of other workstation tasks and the logistics situation in the channel.

[0099] This embodiment achieves dynamic adaptation of resource allocation and task execution status by tracking task completion progress in real time; real-time scheduling of equipment when tasks are completed ahead of schedule improves the utilization rate of handling equipment and reduces resource idleness; flexible adjustment of time windows when tasks are delayed avoids global scheduling chaos caused by the delay of a single task, ensuring dynamic optimization of real-time scheduling and overall execution efficiency, making the scheduling system more in line with the complex and ever-changing actual operation scenarios in the workshop.

[0100] This invention provides a real-time scheduling and management system for digital twin workshop production logistics, comprising:

[0101] The Unified State and Baseline module is used to establish a baseline between unified state data and standard parameters, calculate the consistency deviation value based on the unified state data and standard parameter baseline, and set two levels of deviation judgment thresholds.

[0102] The on-site event scheduling module is used to receive on-site events and determine the scheduling time window based on unified status data, standard parameter baseline, consistency deviation value and on-site events;

[0103] The priority generation module is used to determine priorities based on standard parameter baselines and unified status data, and generate a production execution list.

[0104] The material handling task allocation module is used to generate a set of material handling tasks from the production to be executed list, and determine the allocation results of material handling equipment and material handling tasks under the constraints of load, power consumption and clamping compatibility;

[0105] The path planning module is used to perform conflict-free path planning on the channel based on the allocation results of handling equipment and handling tasks, and to form a time and space occupancy table.

[0106] The consistency check module is used to perform cross-consistency checks based on consistency deviation values, time and space occupancy tables and the upper limit of each workstation queue, and to generate a production operation sequence and a handling equipment task that can be issued based on the first threshold and the second threshold, and to recalculate local objects involving deviations.

[0107] The execution and receipt module is used to execute on-site operations based on the available production operation sequence, available handling equipment tasks, and time and space occupancy table, collect receipt data, and update unified status data; when the consistency deviation value is higher than the second threshold, consistency recovery is performed.

[0108] It should be noted that the interval and threshold sizes are set for ease of comparison. The size of the threshold depends on the amount of sample data and the base number set by those skilled in the art for each set of sample data, as long as it does not affect the proportional relationship between the parameter and the quantized value. Furthermore, the above formulas are all dimensionless calculations, and the formulas are derived from software simulations using a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0109] The embodiments of the present invention have been described above, but the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms based on the guidance of the present embodiments, all of which are within the protection scope of the present embodiments.

Claims

1. A method for real-time scheduling and management of production logistics in a digital twin workshop, characterized in that, Includes the following steps: Step 1: Establish a baseline of unified state data and standard parameters, calculate the consistency deviation value based on the unified state data and standard parameter baseline, and set two levels of deviation judgment thresholds; Step 2: Receive field events and determine the scheduling time window based on unified status data, standard parameter baselines, consistency deviation values, and field events, including: Step 11: Obtain on-site events from the production workshop, where each on-site event contains an event type and a timestamp; organize the on-site events into a set according to the event type and in chronological order, and assign a unique identifier and a corresponding timestamp to each event to form an on-site event set; Step 12: Project the real-time queue information in the unified status data onto each channel, calculate the proportion of the excess capacity of each channel to the total capacity of all channels, and obtain the channel congestion coefficient. Step 13: The scheduling time window is obtained by weighting the basic time window, the channel congestion coefficient, and the event impact coefficient. The event impact coefficient is determined based on the type and number of on-site events. Step 3: Determine priorities based on standard parameter baselines and unified status data, and generate a production to-do list; Step 4: Generate a set of handling tasks from the production to be executed list, and determine the allocation results of handling equipment and handling tasks under the constraints of load, power consumption and clamping compatibility; Step 5: Based on the allocation results of handling equipment and handling tasks, perform conflict-free path planning on the channel to form a time-space occupancy table; Step 6: Perform cross-consistency checks based on the consistency deviation value, the time-space occupancy table, and the upper limit of each workstation queue. Generate assignable production operation sequences and assignable handling equipment tasks based on the first and second thresholds. Recalculate local objects involving deviations, including: Step 51: Calculate the number of materials fed and discharged within the scheduling time window, and use the current queue length to add the number of materials fed and subtract the number of materials discharged to obtain the new queue length for the workstation; Step 52: When the consistency deviation value is less than or equal to the first threshold, there is no conflict in the time and space occupancy table of all channels, and the new queue length of all workstations is less than or equal to the upper limit of the queue, generate a production operation sequence that can be issued and a handling equipment task that can be issued, and issue the scheduling plan. Step 53: When the consistency deviation value is greater than the first threshold and less than or equal to the second threshold, perform a partial recalculation on the workstations, handling equipment and channels related to the deviation, and recalculate the logistics task assignment and route planning. Step 7: Perform on-site operations based on the available production operation sequence, available handling equipment tasks, and time and space occupancy table; collect receipt data and update unified status data; when the consistency deviation value is higher than the second threshold, perform consistency restoration. The production side executes operations based on the available production operation sequence, while the logistics side executes equipment handling based on the available handling equipment tasks and the time and space occupancy table. On-site, feedback data is collected in real time, including the unique identifier of the task, event type, timestamp, number of completed processes, and workstation location. The feedback data is updated to the unified status data in real time. If a path conflict occurs between the handling equipment, it is handled according to the static yielding rules and the time and space occupancy table is updated synchronously. When the consistency deviation value is greater than the second threshold, the unified status data is corrected using the receipt data.

2. The method for real-time scheduling and management of production logistics in a digital twin workshop according to claim 1, characterized in that, Establish a unified state data and standard parameter baseline, and calculate the consistency deviation value based on the unified state data and standard parameter baseline, including: Collect the quantity of work-in-process, the length of each workstation queue, the location of handling equipment, and the operating status of the equipment, and align the timestamps to form unified status data; record the process set, workstation set, standard processing time, loading and unloading and changeover time, channel capacity, and upper limit of workstation queue according to a fixed parameter structure as a standard parameter baseline; The field value of each field in the unified state data is compared one by one with the virtual value of the corresponding field in the twin virtual state to generate the difference quantity; all the difference quantities are linearly weighted and accumulated according to the preset weight and normalization coefficient to form the consistency deviation value.

3. The method for real-time scheduling and management of production logistics in a digital twin workshop according to claim 1, characterized in that, Based on standard parameter baselines and unified status data, task priorities are determined, and a production to-do list is generated, including: Step 21: Calculate the sum of the standard processing times of all remaining processes in the order as the remaining time for each production task, and calculate the difference between the order deadline and the remaining time as the slack of the order. Step 22: Calculate the absolute difference between the current queue length and the target queue length at each workstation, and divide it by the target queue length to obtain the queue deviation for each workstation. Step 23: By weighted summing of order slackness, workstation queue deviation and task urgency, the priority of each task is obtained, and a production list to be executed is generated in order of priority.

4. The real-time scheduling and management method for production logistics in a digital twin workshop according to claim 1, characterized in that, A set of handling tasks is generated from the production pending list, and the allocation of handling equipment and tasks is determined under constraints of load capacity, power consumption, and clamping compatibility, including: Step 31: Based on the workstations to be processed for each order in the production pending list, generate corresponding handling tasks to form a handling task set. Each handling task includes a material pick-up point, a material feeding point, and a time window. Step 32: Calculate the sum of the distance from the location of the handling equipment to the picking point and the distance from the picking point to the feeding point, divide it by the speed of the handling equipment, and add the loading and unloading time of the handling task and the docking time of the handling equipment after completing the task to obtain the handling cost. Step 33: Under the conditions of satisfying load constraints, power constraints, and clamping compatibility constraints, match the handling tasks with the handling equipment, calculate the sum of the handling costs of all handling tasks, obtain the total cost, and obtain the final allocation result by minimizing the total cost.

5. The real-time scheduling and management method for production logistics in a digital twin workshop according to claim 1, characterized in that, Based on the allocation results of handling equipment and handling tasks, conflict-free path planning is performed on the channel to form a time-space occupancy table, including: Step 41: Based on the allocation results of handling equipment and handling tasks, calculate the path of each handling equipment, including from the location of the handling equipment to the picking point, from the picking point to the feeding point, and from the feeding point to the next task location of the handling equipment. Use the shortest path algorithm to calculate the travel time of each path. Step 42: Based on the length of the transport equipment path and the equipment travel speed, calculate the time occupied by each path and the travel time of the transport equipment through the path to obtain the departure time. Based on the entry and exit positions of the path, record the time and space occupancy of each path and generate a time and space occupancy table. Step 43: By detecting the time and space occupancy of the transport equipment path, determine whether there is a path conflict. If there is a conflict, prioritize adjusting the path with lower priority and use the second shortest path for adjustment.

6. The method for real-time scheduling and management of production logistics in a digital twin workshop according to claim 1, characterized in that, During the production process, the progress of each task is tracked in real time, and the resource allocation of handling equipment and workstations is automatically adjusted according to the completion status of the tasks. When a workstation completes its task ahead of schedule, the handling equipment at that workstation is automatically scheduled to perform the next task. When a workstation task is delayed, the scheduling time window of the task is adjusted.

7. A real-time scheduling and management system for production logistics in a digital twin workshop, characterized in that, The method for real-time scheduling and management of production logistics in a digital twin workshop as described in any one of claims 1-6 includes: The Unified State and Baseline module is used to establish a baseline between unified state data and standard parameters, calculate the consistency deviation value based on the unified state data and standard parameter baseline, and set two levels of deviation judgment thresholds. The on-site event scheduling module is used to receive on-site events and determine the scheduling time window based on unified status data, standard parameter baseline, consistency deviation value and on-site events; The priority generation module is used to determine priorities based on standard parameter baselines and unified status data, and generate a production execution list. The material handling task allocation module is used to generate a set of material handling tasks from the production to be executed list, and determine the allocation results of material handling equipment and material handling tasks under the constraints of load, power consumption and clamping compatibility; The path planning module is used to perform conflict-free path planning on the channel based on the allocation results of handling equipment and handling tasks, and to form a time and space occupancy table. The consistency check module is used to perform cross-consistency checks based on consistency deviation values, time and space occupancy tables and the upper limit of each workstation queue, and to generate a production operation sequence and a handling equipment task that can be issued based on the first threshold and the second threshold, and to recalculate local objects involving deviations. The execution and receipt module is used to execute on-site operations based on the available production operation sequence, available handling equipment tasks, and time and space occupancy table, collect receipt data, and update unified status data; when the consistency deviation value is higher than the second threshold, consistency recovery is performed.

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